Paper: Ranking Algorithms For Named Entity Extraction: Boosting And The Voted Perceptron

ACL ID P02-1062
Title Ranking Algorithms For Named Entity Extraction: Boosting And The Voted Perceptron
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2002
Authors

This paper describes algorithms which rerank the top N hypotheses from a maximum-entropy tagger, the applica- tion being the recovery of named-entity boundaries in a corpus of web data. The first approach uses a boosting algorithm for ranking problems. The second ap- proach uses the voted perceptron algo- rithm. Both algorithms give compara- ble, significant improvements over the maximum-entropy baseline. The voted perceptron algorithm can be considerably more efficient to train, at some cost in computation on test examples.